Legged locomotion holds the premise of universal mobility, a critical capability for many real-
world robotic applications. Both model-based and learning-based approaches have …

In this survey, we review the recent advances in control design methods for robotic multi-
agent systems (MAS), focusing on learning-based methods with safety considerations. We …

This paper presents a data-driven strategy to streamline the deployment of model-based
controllers in legged robotic hardware platforms. Our approach leverages a model-free safe …

When learning policies for robotic systems from data, safety is a major concern, as violation
of safety constraints may cause hardware damage. SafeOpt is an efficient Bayesian …

Deployment of Reinforcement Learning (RL) algorithms for robotics applications in the real
world requires ensuring the safety of the robot and its environment. Safe Robot RL (SRRL) is …

Learning optimal control policies directly on physical systems is challenging. Even a single
failure can lead to costly hardware damage. Most existing model-free learning methods that …

Safety is a fundamental property for the real-world deployment of robotic platforms. Any
control policy should avoid dangerous actions that could harm the environment, humans, or …

The partially observable constrained optimization problems (POCOPs) impede data-driven
optimization techniques since an infeasible solution of POCOPs can provide little …

The airfoil design optimization often encounters unknown constraints that are unquantifiable,
which in turn pose challenges for traditional constrained Bayesian optimization (BO) …

In this paper, we propose a multi-domain control parameter learning framework that
combines Bayesian Optimization (BO) and Hybrid Zero Dynamics (HZD) for locomotion …